Our method can be straightforwardly deployed wherever regular dropout can be utilized. To test its general applicability and scalability, we apply the proposed method to three representative types of NN layers: fully connected, convolutional, and attention layers with applications on MNIST [1]}, CIFAR [2]}, ImageNet [3]}, and VQA-v2 [4]}.
To investigate the model's robustness to noise, we also construct noisy versions of datasets by adding Gaussian noises to image inputs [5]}.
